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Advances in Differential Evolution pp 173–196Cite as

Multi-objective Optimization Using Differential Evolution: A Survey of the State-of-the-Art

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 143))

Summary

Differential Evolution is currently one of the most popular heuristics to solve single-objective optimization problems in continuous search spaces. Due to this success, its use has been extended to other types of problems, such as multi-objective optimization. In this chapter, we present a survey of algorithms based on differential evolution which have been used to solve multi-objective optimization problems. Their main features are described and, based precisely on them, we propose a taxonomy of approaches. Some theoretical work found in the specialized literature is also provided. To conclude, based on our findings, we suggest some topics that we consider to be promising paths for future research in this area.

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Authors and Affiliations

  1. Laboratorio Nacional de Informática Avanzada (LANIA A.C.),  , Rébsamen 80, Centro, Xalapa, Veracruz, 91000, Mexico

    Efrén Mezura-Montes

  2. Universidad Veracruzana, Facultad de Estadística e Informática, Avenida Xalapa s/n esquina Ávila-Camacho, Xalapa, Veracruz, 91020, Mexico

    Margarita Reyes-Sierra

  3. CINVESTAV-IPN (Evolutionary Computation Group) Departamento de Computación,  , Av. IPN No. 2508, Col. San Pedro Zacatenco, México D.F., 07360, Mexico

    Carlos A. Coello Coello

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  1. Efrén Mezura-Montes
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  2. Margarita Reyes-Sierra
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  3. Carlos A. Coello Coello
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Uday K. Chakraborty

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Mezura-Montes, E., Reyes-Sierra, M., Coello, C.A.C. (2008). Multi-objective Optimization Using Differential Evolution: A Survey of the State-of-the-Art. In: Chakraborty, U.K. (eds) Advances in Differential Evolution. Studies in Computational Intelligence, vol 143. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68830-3_7

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  • DOI: https://doi.org/10.1007/978-3-540-68830-3_7

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